Affiliation:
1. Anna University, Chennai, Tamilnadu, India
Abstract
Crossover is an important operation in the Genetic Algorithms (GA). Crossover operation is responsible for producing offspring for the next generation so as to explore a much wider area of the solution space. There are many crossover operators designed to cater to different needs of different optimization problems. Despite the many analyses, it is still difficult to decide which crossover to use when. In this article, we have considered the various existing crossover operators based on the application for which they were designed for and the purpose that they were designed for. We have classified the existing crossover operators into two broad categories, namely (1) Crossover operators for representation of applications -- where the crossover operators designed to suit the representation aspect of applications are discussed along with how the crossover operators work and (2) Crossover operators for improving GA performance of applications -- where crossover operators designed to influence the quality of the solution and speed of GA are discussed. We have also come up with some interesting future directions in the area of designing new crossover operators as a result of our survey.
Funder
Anna Centenary Research Fellowship
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Reference129 articles.
1. Using new variation crossover operator of genetic algorithm for solving the Traveling Salesmen Problem;Agarwal Tisha;MIT International Journal of Computer Science and Information Technology,2013
2. Natural Encoding for Evolutionary Supervised Learning
3. Supplementary crossover operator for genetic algorithms based on the center-of-gravity paradigm;Angelov Plamen;Control and Cybernetics,2001
4. Repeat distributions from unequal crossovers
Cited by
63 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献